Type 1 diabetes is a chronic, life-threatening disease that is caused by failure of the pancreas to deliver the hormone insulin, which is otherwise made and secreted by the beta cells of the pancreatic islets of Langerhans. With the resulting absence of endogenous insulin, people with type 1 diabetes cannot regulate their blood glucose to euglycemic range without exogenous insulin administration. Therefore, it is necessary for people with type 1 diabetes to monitor their blood glucose and administer exogenous insulin several times a day in a relentless effort to maintain their blood glucose near euglycemic range.
The existing blood glucose management devices assist a diabetic patient in managing their blood glucose levels during everyday routine. Some of these devices are insulin pumps that provide continuous delivery of insulin throughout the day. Others are, for example: glucose monitoring devices which measure blood glucose levels along a certain time line i.e. to obtain blood glucose reading; and Artificial Pancreas (AP) systems which automatically modulate insulin delivery (optionally other hormones) according to measured glucose levels.
Insulin pump allows the physician to preset the pump settings to many different basal rates to allow for variation in the patient's lifestyle. In addition, the physician can predetermine the insulin bolus delivery (large dose of insulin) to cover the excess demands of carbohydrate ingestion or to correct high blood glucose levels. These pump settings include: bloods glucose target levels, insulin basal rate; carbohydrate ratio (CR) or factor; correction factor (CF) and constant insulin activity function.
Normally, the physician receives from the patient personalized information which includes the glucose past trace (measured by glucometer in discrete points or using continuous glucose sensor), the insulin that was previously delivered (the detailed log of how many insulin was delivered—in either basal or bolus—over time), and the detailed log of the amount and time of all meals and physical activity of the diabetic patients. The physician thus needs to conduct a retrospective analysis (i.e., look at the log data during the clinical visit) and determine the insulin pump settings based on this information.
Various techniques have been developed aimed at facilitating the operation of the insulin delivery pump device. Such techniques are disclosed for example in the following patent publications:
US Publication No. 2008/0228056 discloses an apparatus comprising a user interface configured to generate an electrical signal to start a basal insulin rate test when prompted by a user, an input configured to receive sampled blood glucose data of a patient that is obtained during a specified time duration, including a time duration during delivery of insulin according to a specified basal insulin rate pattern, and a controller communicatively coupled to the input and the user interface. The controller includes an insulin calculation module.
U.S. Pat. No. 7,751,907 discloses an apparatus comprising a controller; the controller includes an input/output (I/O) module and a rule module; the I/O module is configured to present a question for a patient when communicatively coupled to a user interface and receive patient information in response to the question via the user interface; the rule module is configured to apply a rule to the patient information and generate a suggested insulin pump setting from application of the rule.
US Publication No. 2008/0206799 discloses an apparatus comprising a user interface configured to generate an electrical signal to begin a carbohydrate ratio test when prompted by a user, an input configured to receive sampled blood glucose data of a patient that is obtained during specified time duration, and a controller in electrical communication with the input and the user interface. The controller includes a carbohydrate ratio suggestion module.
U.S. Pat. No. 7,734,323 discloses an apparatus comprising a user interface configured to generate an electrical signal to begin determination of an effective correction factor when prompted by a user, an input configured to receive sampled blood glucose data of a patient that is obtained during a specified time duration, and a controller in electrical communication with the input and the user interface. The controller includes a correction factor suggestion module.
On the other side, the artificial pancreas systems are usually based either on traditional linear control theory or rely on mathematical models of glucose-insulin dynamics. The most common techniques are based on proportional-integral-derivative control (PID) [1] and model predictive control (MPC) [2-5]. However, the nonlinearity, complexity and uncertainty of the biological system along with the inherited delay and deviation of the measuring devices, makes difficult to define a model and correctly evaluate the physiological behavior of the individual patient [1-3, 5]. In addition, because most of the control algorithms are not amenable to multiple inputs and multiple outputs, the measured blood glucose level is generally, the only input implemented, and insulin delivery is the only implemented output.
The PID control algorithm produces an insulin profile similar to the secretion profile done by the beta cells extrapolated by three components [1]. Some controllers include a subset of components, for example, a proportional-derivative (PD) controller includes the proportional and derivative components to improve robustness. Both PID and PD use the measured blood glucose (BG) level as the only input and ignore other parameters, such as previous administered insulin doses. The MPC is based on mathematical model and equations which describes the glucose level response to different insulin doses and carbohydrate consumption. As the response to different insulin treatment is implied by the set of equations, an optimal treatment may be found and applied accordingly. The mathematical model is subject specific, and depends upon system identification phase to estimate the required parameters [3]. The main drawback of MPC in relation to glucose control is the need of a good crisp mathematical model and a good method to estimate its parameters in order to describe the physiological behavior of the patient. However, due to the complexity of biological systems, these models are subject to extreme uncertainties, which make it very hard to evaluate and define the model properly. Most of the attempts in the past to develop Subcutaneous (S.C.) closed loop system used linear control methodology to control the non-linear biological system [2, 5] and disregarded the uncertainty of the biological system and the measuring devices. In addition, it is quite difficult to implement multiple inputs and multiple outputs using these methods.